OpenRaiser/Intern-Atlas huggingface dataset:
Energy Efficiency of Generalized Spatial Modulation Aided Massive MIMO Systems
Venue: arXiv cs.NI 2019
Year: 2019

One of focuses in green communication studies is the energy efficiency (EE) of massive multiple-input multiple-output (MIMO) systems. Although the massive MIMO technology can improve the spectral efficiency (SE) of cellular networks by configuring a large number of antennas at base stations (BSs), the energy consumption of radio frequency (RF) chains increases dramatically. The increment of energy consumption is caused by the increase of RF chain number to match the antenna number in massive MIMO communication systems. To overcome this problem, a generalized spatial modulation (GSM) solution is presented to simultaneously reduce the number of RF chains and maintain the SE of massive MIMO communication systems. A EE model is proposed to estimate the transmission and computation power of massive MIMO communication systems with GSM. Simulation results demonstrate that the EE of massive MIMO communication systems with GSM outperforms the massive MIMO communication systems without GSM. Besides, the computation power consumed by massive MIMO communication systems with GSM is effectively reduced.
Examining the Presence of Gender Bias in Customer Reviews Using Word Embedding
Venue: arXiv cs.CL 2019
Year: 2019

Humans have entered the age of algorithms. Each minute, algorithms shape countless preferences from suggesting a product to a potential life partner. In the marketplace algorithms are trained to learn consumer preferences from customer reviews because user-generated reviews are considered the voice of customers and a valuable source of information to firms. Insights mined from reviews play an indispensable role in several business activities ranging from product recommendation, targeted advertising, promotions, segmentation etc. In this research, we question whether reviews might hold stereotypic gender bias that algorithms learn and propagate Utilizing data from millions of observations and a word embedding approach, GloVe, we show that algorithms designed to learn from human language output also learn gender bias. We also examine why such biases occur: whether the bias is caused because of a negative bias against females or a positive bias for males. We examine the impact of gender bias in reviews on choice and conclude with policy implications for female consumers, especially when they are unaware of the bias, and the ethical implications for firms.
Competitive Experience Replay
Venue: arXiv cs.LG 2019
Year: 2019

Deep learning has achieved remarkable successes in solving challenging reinforcement learning (RL) problems when dense reward function is provided. However, in sparse reward environment it still often suffers from the need to carefully shape reward function to guide policy optimization. This limits the applicability of RL in the real world since both reinforcement learning and domain-specific knowledge are required. It is therefore of great practical importance to develop algorithms which can learn from a binary signal indicating successful task completion or other unshaped, sparse reward signals. We propose a novel method called competitive experience replay, which efficiently supplements a sparse reward by placing learning in the context of an exploration competition between a pair of agents. Our method complements the recently proposed hindsight experience replay (HER) by inducing an automatic exploratory curriculum. We evaluate our approach on the tasks of reaching various goal locations in an ant maze and manipulating objects with a robotic arm. Each task provides only binary rewards indicating whether or not the goal is achieved. Our method asymmetrically augments these sparse rewards for a pair of agents each learning the same task, creating a competitive game designed to drive exploration. Extensive experiments demonstrate that this method leads to faster converge and improved task performance.
Comparison of Patch-Based Conditional Generative Adversarial Neural Net Models with Emphasis on Model Robustness for Use in Head and Neck Cases for MR-Only planning
Venue: arXiv cs.CV 2019
Year: 2019

A total of twenty paired CT and MR images were used in this study to investigate two conditional generative adversarial networks, Pix2Pix, and Cycle GAN, for generating synthetic CT images for Headand Neck cancer cases. Ten of the patient cases were used for training and included such common artifacts as dental implants; the remaining ten testing cases were used for testing and included a larger range of image features commonly found in clinical head and neck cases. These features included strong metal artifacts from dental implants, one case with a metal implant, and one case with abnormal anatomy. The original CT images were deformably registered to the mDixon FFE MR images to minimize the effects of processing the MR images. The sCT generation accuracy and robustness were evaluated using Mean Absolute Error (MAE) based on the Hounsfield Units (HU) for three regions (whole body, bone, and air within the body), Mean Error (ME) to observe systematic average offset errors in the sCT generation, and dosimetric evaluation of all clinically relevant structures. For the test set the MAE for the Pix2Pix and Cycle GAN models were 92.4 $\pm$ 13.5 HU, and 100.7 $\pm$ 14.6 HU, respectively, for the body region, 166.3 $\pm$ 31.8 HU, and 184 $\pm$ 31.9 HU, respectively, for the bone region, and 183.7 $\pm$ 41.3 HU and 185.4 $\pm$ 37.9 HU for the air regions. The ME for Pix2Pix and Cycle GAN were 21.0 $\pm$ 11.8 HU and 37.5 $\pm$ 14.9 HU, respectively. Absolute Percent Mean/Max Dose Errors were less than 2% for the PTV and all critical structures for both models, and DRRs generated from these models looked qualitatively similar to CT generated DRRs showing these methods are promising for MR-only planning.
A New Framework of Authentication Over Cloud Computing
Venue: arXiv cs.CR 2019
Year: 2019

The growth of local data annually implies extra charges for the customers, which makes their business slowing down. Cloud computing paradigm comes with new technologies that offer a very economic and cost-effective solution, but the organizations are slow in accepting it due to security issues and challenges associated with it. Security is one of the major issues which hamper the growth of cloud and it is highly crucial to protect the sensitive data and systems in thecloud setting inorder toensure theprivacy ofits users. This work explores the new directions in cloud computing security, while highlighting the correct selection of these fundamental technologies from cryptographic point of view.
Shallow EDSLs and Object-Oriented Programming: Beyond Simple Compositionality
Venue: arXiv cs.PL 2019
Year: 2019

Context: Embedded Domain-Specific Languages (EDSLs) are a common and widely used approach to DSLs in various languages, including Haskell and Scala. There are two main implementation techniques for EDSLs: shallow embeddings and deep embeddings. Inquiry: Shallow embeddings are quite simple, but they have been criticized in the past for being quite limited in terms of modularity and reuse. In particular, it is often argued that supporting multiple DSL interpretations in shallow embeddings is difficult. Approach: This paper argues that shallow EDSLs and Object-Oriented Programming (OOP) are closely related. Gibbons and Wu already discussed the relationship between shallow EDSLs and procedural abstraction, while Cook discussed the connection between procedural abstraction and OOP. We make the transitive step in this paper by connecting shallow EDSLs directly to OOP via procedural abstraction. The knowledge about this relationship enables us to improve on implementation techniques for EDSLs. Knowledge: This paper argues that common OOP mechanisms (including inheritance, subtyping, and type-refinement) increase the modularity and reuse of shallow EDSLs when compared to classical procedural abstraction by enabling a simple way to express multiple, possibly dependent, interpretations. Grounding: We make our arguments by using Gibbons and Wu's examples, where procedural abstraction is used in Haskell to model a simple shallow EDSL. We recode that EDSL in Scala and with an improved OO-inspired Haskell encoding. We further illustrate our approach with a case study on refactoring a deep external SQL query processor to make it more modular, shallow, and embedded. Importance: This work is important for two reasons. Firstly, from an intellectual point of view, this work establishes the connection between shallow embeddings and OOP, which enables a better understanding of both concepts. Secondly, this work illustrates programming techniques that can be used to improve the modularity and reuse of shallow EDSLs.
Uncovering Political Promotion in China: A Network Analysis of Patronage Relationship in Autocracy
Venue: arXiv cs.SI 2019
Year: 2019

Understanding patronage networks in Chinese Bureaucracy helps us quantify the promotion mechanism underlying autocratic political systems. Although there are qualitative studies analyzing political promotions, few use quantitative methods to model promotions and make inferences on the fitted mathematical model. Using publicly available datasets, we implement network analysis techniques to advance scholarly understanding of patronage networks in autocratic regimes, using the Chinese bureaucracy as an example. Using graph-based and non-graph-based features, we design three studies to examine drivers of political promotions. We find that careers of politicians are closely associated with their genders, home origins, and positions in the patronage networks.
Uniform-in-Time Weak Error Analysis for Stochastic Gradient Descent Algorithms via Diffusion Approximation
Venue: arXiv cs.LG 2019
Year: 2019

Diffusion approximation provides weak approximation for stochastic gradient descent algorithms in a finite time horizon. In this paper, we introduce new tools motivated by the backward error analysis of numerical stochastic differential equations into the theoretical framework of diffusion approximation, extending the validity of the weak approximation from finite to infinite time horizon. The new techniques developed in this paper enable us to characterize the asymptotic behavior of constant-step-size SGD algorithms for strongly convex objective functions, a goal previously unreachable within the diffusion approximation framework. Our analysis builds upon a truncated formal power expansion of the solution of a stochastic modified equation arising from diffusion approximation, where the main technical ingredient is a uniform-in-time weak error bound controlling the long-term behavior of the expansion coefficient functions near the global minimum. We expect these new techniques to greatly expand the range of applicability of diffusion approximation to cover wider and deeper aspects of stochastic optimization algorithms in data science.
Enabling Robots to Infer how End-Users Teach and Learn through Human-Robot Interaction
Venue: arXiv cs.RO 2019
Year: 2019

During human-robot interaction (HRI), we want the robot to understand us, and we want to intuitively understand the robot. In order to communicate with and understand the robot, we can leverage interactions, where the human and robot observe each other's behavior. However, it is not always clear how the human and robot should interpret these actions: a given interaction might mean several different things. Within today's state-of-the-art, the robot assigns a single interaction strategy to the human, and learns from or teaches the human according to this fixed strategy. Instead, we here recognize that different users interact in different ways, and so one size does not fit all. Therefore, we argue that the robot should maintain a distribution over the possible human interaction strategies, and then infer how each individual end-user interacts during the task. We formally define learning and teaching when the robot is uncertain about the human's interaction strategy, and derive solutions to both problems using Bayesian inference. In examples and a benchmark simulation, we show that our personalized approach outperforms standard methods that maintain a fixed interaction strategy.
Volumetric Spline Parameterization for Isogeometric Analysis
Venue: arXiv cs.GR 2019
Year: 2019

Given the spline representation of the boundary of a three dimensional domain, constructing a volumetric spline parameterization of the domain (i.e., a map from a unit cube to the domain) with the given boundary is a fundamental problem in isogeometric analysis. A good domain parameterization should satisfy the following criteria: (1) the parameterization is a bijective map; and (2) the map has lowest possible distortion. However, none of the state-of-the-art volumetric parameterization methods has fully addressed the above issues. In this paper, we propose a three-stage approach for constructing volumetric parameterization satisfying the above criteria. Firstly, a harmonic map is computed between a unit cube and the computational domain. Then a bijective map modeled by a max-min optimization problem is computed in a coarse-to-fine way, and an algorithm based on divide and conquer strategy is proposed to solve the optimization problem efficiently. Finally, to ensure high quality of the parameterization, the MIPS (Most Isometric Parameterizations) method is adopted to reduce the conformal distortion of the bijective map. We provide several examples to demonstrate the feasibility of our approach and to compare our approach with some state-of-the-art methods. The results show that our algorithm produces bijective parameterization with high quality even for complex domains.
Polarization and Fluctuations in Signed Social Networks
Venue: arXiv cs.SI 2019
Year: 2019

Much recent research on social networks has focused on the modeling and analysis of how opinions evolve as a function of interpersonal relationships. It is also of great interest to model and understand the implications of friendly and antagonistic relationships. In this paper, we propose a new, simple and intuitive model that incorporates the socio-psychological phenomenon of the boomerang effect in opinion dynamics. We establish that, under certain conditions on the structure of the signed network that corresponds to the so-called structural balance property, the opinions in the network polarize. Compared to other models in the literature, our model displays a richer and perhaps more intuitive behavior of the opinions when the social network does not satisfy structural balance. In particular, we analyze signed networks in which the opinions show persistent fluctuations (including the case of the so-called clustering balance).
Big Data and Social/Medical Sciences: State of the Art and Future Trends
Venue: arXiv cs.CY 2019
Year: 2019

The explosion of data on the internet is a direct corollary of the social media platform. With petabytes of data being generated by end users, the researchers have access to unprecedented amount of data (Big Data). Such data provides an insight into user mental state and hence can be utilized to produce clinical evidence. This lofty goal requires a thorough understanding of not only the mental health issues but also the technology trends underlying the Big Data and how they can be leveraged effectively. The paper looks at various such concepts, provides an overview and enumerates the work that has been done in this realm. Furthermore, we provide guidelines for future work that will help in streamlining the Big Data use in social/medical sciences.
When Collaborative Filtering Meets Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we study a multi-step interactive recommendation problem, where the item recommended at current step may affect the quality of future recommendations. To address the problem, we develop a novel and effective approach, named CFRL, which seamlessly integrates the ideas of both collaborative filtering (CF) and reinforcement learning (RL). More specifically, we first model the recommender-user interactive recommendation problem as an agent-environment RL task, which is mathematically described by a Markov decision process (MDP). Further, to achieve collaborative recommendations for the entire user community, we propose a novel CF-based MDP by encoding the states of all users into a shared latent vector space. Finally, we propose an effective Q-network learning method to learn the agent's optimal policy based on the CF-based MDP. The capability of CFRL is demonstrated by comparing its performance against a variety of existing methods on real-world datasets.
Event Loops as First-Class Values: A Case Study in Pedagogic Language Design
Venue: arXiv cs.PL 2019
Year: 2019

The World model is an existing functional input-output mechanism for event-driven programming. It is used in numerous popular textbooks and curricular settings. The World model conflates two different tasks -- the definition of an event processor and its execution -- into one. This conflation imposes a significant (even unacceptable) burden on student users in several educational settings where we have tried to use it, e.g., for teaching physics. While it was tempting to pile on features to address these issues, we instead used the Scheme language design dictum of removing weaknesses that made them seem necessary. By separating the two tasks above, we arrived at a slightly different primitive, the reactor, as our basis. This only defines the event processor, and a variety of execution operators dictate how it runs. The new design enables programmatic control over event-driven programs. This simplifies reflecting on program behavior, and eliminates many unnecessary curricular dependencies imposed by the old design. This work has been implemented in the Pyret programming language. The separation of concerns has enabled new curricula, such as the Bootstrap:Physics curriculum, to take flight. Thousands of students use this new mechanism every year. We believe that reducing impedance mismatches improves their educational experience.
Online Multi-Object Tracking with Dual Matching Attention Networks
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we propose an online Multi-Object Tracking (MOT) approach which integrates the merits of single object tracking and data association methods in a unified framework to handle noisy detections and frequent interactions between targets. Specifically, for applying single object tracking in MOT, we introduce a cost-sensitive tracking loss based on the state-of-the-art visual tracker, which encourages the model to focus on hard negative distractors during online learning. For data association, we propose Dual Matching Attention Networks (DMAN) with both spatial and temporal attention mechanisms. The spatial attention module generates dual attention maps which enable the network to focus on the matching patterns of the input image pair, while the temporal attention module adaptively allocates different levels of attention to different samples in the tracklet to suppress noisy observations. Experimental results on the MOT benchmark datasets show that the proposed algorithm performs favorably against both online and offline trackers in terms of identity-preserving metrics.
East Asians with Internet Addiction: Prevalence Rates and Support Use Patterns
Venue: arXiv cs.HC 2019
Year: 2019

The issue of Internet addiction has become a serious social and health issue in East Asian countries. There are only a few treatment programs for Internet addiction, and their effectiveness with people from East Asian remains unclear. As support and treatment develop, it is necessary to understand cultural preferences for dealing with this concern. Using data from the East Asian Social Survey (EASS), this study examined preferred sources of assistance for help with internet use problems in four countries - China, Japan, South Korea, and Taiwan. Preferences for kin versus non-kin support, use of alternative medicine, and professional mental health assistance were examined, as were between-country differences in support preferences. The results indicate a strong preference for seeking assistance from close relatives, followed by non-kin support (i.e., close friends and co-participants in religious institutions), alternative medicine, and professional mental health services, respectively. While there is a strong preference for family support, over 80% of survey respondents were open to seeking formal or informal mental health support outside the family. There were some significant differences between countries, with South Koreans being more likely to seek non-kin support and professional support for internet addiction concerns compared to Chinese. These differences are discussed in the context of cultural and policy developments in East Asian countries. Findings suggest the need for a more holistic approach to treating low mental health concerns.
Learning Linear Dynamical Systems with Semi-Parametric Least Squares
Venue: arXiv cs.LG 2019
Year: 2019

We analyze a simple prefiltered variation of the least squares estimator for the problem of estimation with biased, semi-parametric noise, an error model studied more broadly in causal statistics and active learning. We prove an oracle inequality which demonstrates that this procedure provably mitigates the variance introduced by long-term dependencies. We then demonstrate that prefiltered least squares yields, to our knowledge, the first algorithm that provably estimates the parameters of partially-observed linear systems that attains rates which do not not incur a worst-case dependence on the rate at which these dependencies decay. The algorithm is provably consistent even for systems which satisfy the weaker marginal stability condition obeyed by many classical models based on Newtonian mechanics. In this context, our semi-parametric framework yields guarantees for both stochastic and worst-case noise.
Google Classroom as a Tool of Support of Blended Learning for Geography Students
Venue: arXiv cs.CY 2019
Year: 2019

The article reveals the experience of organizing blended learning for geography students using Google Classroom, and discloses its potential uses in the study of geography. For the last three years, the authors have tested such inclass and distance courses as "Cartography and Basics of Topography", "Population Geography", "Information Systems and Technologies in Tourism Industry","Regional Economic and Social World Geography (Europe and the CIS)", "Regional Economic and Social World Geography (Africa, Latin America, Asia,Anglo-America, Australia and Oceania)", "Socio-Economic Cartography". The advantages of using the specified interactive tool during the study of geographical disciplines are highlighted out in the article. As it has been established, the organization of the learning process using Google Classroom ensures the unity of in-class and out-of-class learning; it is designed to realize effective interaction of the subjects learning in real time; to monitor the quality of training and control the students' learning achievements in class as well as out of it, etc. The article outlines the disadvantages that should be taken into account when organizing blended learning using Google Classroom, including the occasional predominance of students' external motivation in education and their low level of readiness for work in the classroom; insufficient level of material and technical support in some classrooms; need for out-of-class pedagogical support; lack of guidance on the content aspect of Google Classroom pages, etc. Through the test series conducted during 2016-2017, an increase in the number of geography students with a sufficient level of academic achievements and a decrease of those with a low level of it was revealed.